Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation

DOI10.1007/978-3-319-42972-4_23zbMath1422.62020OpenAlexW2495728588MaRDI QIDQ5348623

Roberto Rocci, Roberto Di Mari, Stefano Antonio Gattone

Publication date: 18 August 2017

Published in: Advances in Intelligent Systems and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-42972-4_23

zbMATH Keywords

maximum likelihood estimationcovariance structurelinear regression modelsadaptive constrained approach

Mathematics Subject Classification ID

Cites Work

• A maximum likelihood methodology for clusterwise linear regression
• Root selection in normal mixture models
• A general trimming approach to robust cluster analysis
• Trimmed ML estimation of contaminated mixtures
• Constrained monotone EM algorithms for finite mixture of multivariate Gaussians
• A constrained formulation of maximum-likelihood estimation for normal mixture distributions
• Degeneracy in the maximum likelihood estimation of univariate Gaussian mixtures with EM.
• Identifiability of models for clusterwise linear regression
• Robust clusterwise linear regression through trimming
• A likelihood-based constrained algorithm for multivariate normal mixture models
• A note on Phillips (1991): ``A constrained maximum likelihood approach to estimating switching regressions
• Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters
• Robust Linear Clustering
• Estimating Mixtures of Normal Distributions and Switching Regressions
• Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model
• Penalized Maximum Likelihood Estimator for Normal Mixtures
• Estimating the components of a mixture of normal distributions
• A New Approach to Estimating Switching Regressions
• Robust Statistics
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